System and Method for Generating Challenge Items for CAPTCHAs

ABSTRACT

Challenge items for an audible based electronic challenge system are generated using a variety of techniques to identify optimal candidates. The challenge items are intended for use in a computing system that discriminates between humans and text to speech (TTS) system.

RELATED APPLICATION DATA

The present application claims the benefit under 35 U.S.C. 119(e) of thepriority date of Provisional Application Ser. No. 61/074,979 filed Jun.23, 2008 which is hereby incorporated by reference. The application isfurther related to the following applications, all of which are filed onthis same date and incorporated by reference herein:

-   -   CAPTCHA Using Challenges Optimized for Distinguishing Between        Humans and Machines; Ser. No. ______ (attorney docket number        2009-1)    -   System & Method for Verifying Origin of Input Through Spoken        Language Analysis Ser. No. ______ (attorney docket number        2009-2)

FIELD OF THE INVENTION

The present invention relates to electronic systems for generatingchallenge items optimized for detecting and differentiating inputprovided by humans and machines. These systems are used primarily inInternet applications for verifying that data originating from a sourceis from a human, and not from an unauthorized computer program/softwareagent/robot. In other applications entities can be fingerprinted todetect unauthorized accesses regardless of their origin.

BACKGROUND CATPCHA (Completely Automatic Public Turing Tests To TellHumans And

Computers Apart) systems are well known in the art. Examples of such areused by Yahoo! (Gimpy type), Xerox PARC (Baffle type); so-called Bongo,Pix and

Pessimal types are also known in the art. One of the first such visualbased systems is described in U.S. patent application Ser. No.10/790,611 to Reshef, which is hereby incorporated by reference herein.

Generally speaking, the goal of visual based CAPTCHAs is to present anoptical image which is only decipherable/comprehensible by a human. Tothis end, the bulk of these systems rely primarily on some combinationof pseudorandom letters and numbers which are placed in front of anobfuscating background, or subjected to visual degradation to make themmachine-unrecognizable. A good background on such technologies can befound in the article “Is it Human or Computer? Defending E-Commerce withCaptchas,” by Clark Pope and Khushpreet Kaur in IT PRO, March-April2005, p. 43-49, which is hereby incorporated by reference herein. Anexample of a typical CAPTCHA of the prior art is shown in FIG. 6. Theperson looking at the image presented would have to determine that thetext shown corresponds to the characters “84EMZ.”

An article entitled What's Up CAPTCHA?—A CAPTCHA Based On ImageOrientation by Gossweiler et al. incorporated by reference herein makesuse of social feedback mechanisms to select appropriate challengematerials for visual CAPTCHs. The integration of aggregated humanfeedback allows for better selection of CAPTCHAs that are best optimizedfor discriminating against machines.

Recently, however, several sophisticated machine vision systems haveachieved significant success in “breaking” the conventional opticalCAPTCHA systems. For an example of such system, see “Recognizing Objectsin Adversarial Clutter: Breaking a Visual CAPTCHA” by Mori and Malik,also incorporated by reference herein and which is available at theUniversity of California Berkeley Computer Science Department website.Thus, traditional forms of CAPTCHA appear to be at risk of becomingobsolete before they gain widespread adoption.

Audio CAPTCHas are also known in the art. For an example of such systemplease see the above article to Pope and Kaur, page 45. Generallyspeaking, these types of systems take a random sequence of recordings ofwords, numbers, etc., combine them, and then ask the user to input—viakeyboard or mouse—whatever is “heard” by the user into the system todetermine if the message is comprehended. A drawback of this approach,of course, is that speech recognizers are improving rapidly; an articleby Reynolds and Heck entitled “Automatic Speaker Recognition RecentProgress, Current Applications, and Future Trends” presented at AAAS2000 Meeting Humans, Computers and Speech Symposium 19 February2000—incorporated by reference herein—makes it clear that machines arein fact better analyzers and recognizers of speech than are humans atthis point. Consequently, audio CAPTCHAs of this type are similarlydoomed to failure at this point.

The Reynolds et al article also notes that speech verification systemsare well-known in the art. These systems are basically used as a form ofhuman biometric analyzer, so that a person can access sensitiveinformation over a communications link using his/her voice. A voiceprint for the particular user is created using a conventional HiddenMarkov Model (HMM) during an enrollment/training session. Later when theuser attempts to access the system—for example, in a banking applicationthe user may wish to transfer funds from an account—the system comparescertain captured audio data from the user against the prior recording tosee if there is a sufficiently close biometric match. Identities aretypically confirmed by measuring such intrinsic personal traits as lungcapacity, nasal passages and larynx size. Again, since speechrecognizers are extremely accurate in evaluating speech data, a veryreliable verification can be made to determine if the identity of theperson matches the prior recorded voice print. Speaker verificationsystems are well-known and are disclosed, for example in such referencesas U.S. Pat. Nos. 5,897,616; 6,681,205 and Publication No. 20030125944which are incorporated by reference herein.

Another article by Shucker—Spoofing and Anti-Spoofing Measures,Information Security Technical Report, Vol. 7, No. 4, pages 56-62, 2002explains that these verification systems are very hard to fool with taperecording equipment and the like, because such systems cannot duplicatethe physical characteristics noted above. Thus, somespeaker-verification technology has ways of testing for “liveness.” Theyspecifically analyze for acoustic patterns suggesting that the voice hasbeen recorded using a process called anti-spoofing. Another applicationof this technique for fingerprinting is also described generally in U.S.Pat. No. 6,851,051 to Bolle et al. which is incorporated by referenceherein. Other biometric techniques for uniquely differentiating humansapart are disclosed in US Publication No. 20050185847A1 to Rowe which isalso incorporated by reference herein.

To date, therefore, while verification systems have been used fordistinguishing between humans, they have been designed or employed on alimited basis for the purpose of distinguishing between a computerspeaking and a human speaking as part of a CAPTCHA type tester/analyzer.This is despite the fact that a recent article entitled “The Artificialof Conversation” published at:http://htmltimes(dot)com/turing-test-machine-intelligence(dot)phpimplies that conventional Turing tests do not even bother examiningcomputer system vocalizations since they are too difficult.

A recent article entitled “Accessible Voice CAPTCHAs for InternetTelephony” by Markkola et al. incorporated by reference herein describesa Skype challenge system that requires the user to speak a number ofrandom digits. This illustrates that there is known value in usingspoken CAPTCHAs.

Some recent filings by Rajakumar (US Publication No. 20070280436,20070282605 and 20060248019) also incorporated by reference herein alsodiscuss the use of a voice database for registering the names of knownfraudsters. Thereafter when a person attempts access the system candetect whether the person calling is already registered and is thereforeblocked based on his/her voiceprint.

A further filing by Maislos et al. (US Publication No. 20090055193)(Ser. No. 12/034,736) is also incorporated by reference herein. TheMaislos system—while purportedly using voice to differentiate betweenhumans and computing systems, and even different demographic groups—isonly recently filed and does not contain many details on how to optimizesuch discrimination, or how to formulate appropriate challenges. Anothercompany identified as Persay is also believed to be researching voicebased CAPTHCA systems; see e.g. www(dot)persay(dot)com and accompanyingliterature for their SPID system.

SUMMARY OF THE INVENTION

An object of the present invention, therefore, is to overcome theaforementioned limitations of the prior art. It will be understood fromthe Detailed Description that the inventions can be implemented in amultitude of different embodiments. Furthermore, it will be readilyappreciated by skilled artisans that such different embodiments willlikely include only one or more of the aforementioned objects of thepresent inventions. Thus, the absence of one or more of suchcharacteristics in any particular embodiment should not be construed aslimiting the scope of the present inventions.

A first aspect of the invention concerns a method of identifying asource of data input to a computing system comprising: receiving speechutterance from an entity related to randomly selected challenge text;wherein the challenge text represents a selected set of one morecontiguous words which when articulated have a measurable difference inacoustical characteristics between a reference human voice and areference computer synthesized voice that exceeds a target threshold;processing the speech utterance with the computing system to computefirst acoustical characteristics of the entity; and generating adetermination of whether the speech utterance originated from a machineor a human.

In preferred embodiments additional steps may be performed including:identifying a first computer synthesized voice that best correlates tothe entity; and selecting the randomly selected challenge text based onan identity of the first entity so as to maximize a difference inacoustical characteristics. In addition in preferred embodiments thechallenge text can be selected in part based on a confirmation from ahuman listener that an articulation of such challenge text originatedfrom a computer synthesized voice. Also preferred embodiments may havethe steps: soliciting utterances from a plurality of separate computingmachines to determine their respective acoustical characteristics; andstoring the plurality of associated acoustical characteristics in adatabase of known computing entities. Multiple samples of individualchallenge sentences are preferably collected. The challenge text ispreferably selected in part based on a difference in time for renderingsuch text into audible form by a human and a computing machine.

Some preferred embodiments include a step: granting or denying access todata and/or a data processing device based on the results of theCAPTCHA, including a signup for an email account or a blog posting. Forothers the step of granting or denying access to an advertisement basedon the determination is performed. Other preferred embodiments perform aseparate automated visual challenge test so that both visual processingand articulation processing is considered in one or more of thedeterminations.

For some applications a prosody score associated with the speechutterance is also preferably considered during step (c). The first testtext preferably consists of a sentence presented in visual form for theentity to articulate, and/or a sentence presented in audible form forthe entity to repeat. The first text data can also consist of a questionpresented in visual form and further includes an image cue associatedwith the question as well as a separate set of acceptable responsespresented in visual form.

In some preferred embodiments an additional step is performed: selectingone or more second computing systems for separately performing theprocess based on a performance and/or cost requirement. These one ormore second computing systems can be selected based on a language spokenby the entity, an IP address or geographic region associated with theentity, an auction process in which such one more second computingsystems bid for the right to process the speech utterance, etc. Thechallenge text can also be selected based on a detected accent and/orgeographic region associated with the entity.

A set of sentences for inclusion as challenge text preferably is basedon an articulation difficulty score for a computer synthesis engine. Inother cases the sentences are automatically generated based on ameasured concatenation difficulty for a set of diphones. In still otherapplications the set of sentences are extracted automatically from acorpus that includes web logs, newspapers, books and/or the Internet.

Another aspect of the invention concerns a method of implementing aCATPCHA (Completely Automatic Public Turing Test To Tell Humans AndComputers Apart) to identify a source of data input to a computingsystem comprising: presenting an image CATPCHA to an entity, which imageCAPTCHA includes one or more visually distorted words, phrases or imagesas a challenge item; receiving a speech utterance from an entity relatedto the challenge text; processing the speech utterance to generate adetermination of whether the speech utterance originated from a machineor a human.

The image CAPTCHA can take various forms, and preferably includes atleast two distinct words. The determination preferably includes a firstscore based on computing acoustical characteristics of the speechutterance, and a second score based on recognizing the speech utteranceto determine if the one or more visually distorted words, phrases orimages are correctly identified. The scoring can also take into accounta time required for the entity to determine the image CAPTCHA. In yetother applications the image CAPTCHA is revealed in distinct stageswhich span a predetermined time period, and with each stage presentingadditional visual information.

Still another aspect concerns a method of identifying a source of datainput to a computing system comprising: associating a first challengeitem with a first set of individual text descriptors; wherein the firstset of text descriptors are based on feedback provided by a group ofhuman reviewers; associating a second challenge item with a second setof individual text descriptors; wherein the second set of individualtext descriptors are also based on feedback provided by a group of humanreviewers; identifying at least a first reference correlation between atleast a first reference text descriptor for the first challenge item anda second reference text descriptor for the second challenge item,including a probability that a human reviewer identifying the firstreference text descriptor when presented with the first challenge itemalso provides the second reference text descriptor when presented withthe second challenge item, or vice-versa; presenting the first challengeitem to an entity as part of an automated access challenge adapted todistinguish humans from computing machines; receiving speech utterancefrom the entity related to the first challenge item to determine a firstinput text descriptor; presenting the second challenge item to theentity as part of the automated access challenge adapted to distinguishhumans from computing machines; receiving speech utterance from theentity related to the second challenge item to determine a second inputtext descriptor; comparing the first and second input text descriptorsto identify the reference correlation between them as measured; andgenerating a determination of whether the speech utterance originatedfrom a machine or a human based on a value of the reference correlation.

The challenge items preferably include an image, a question, ordifferent types, including a first type which includes an image, and asecond type which includes a question. The first and second challengeitems can also be selected based on a value of the referencecorrelation.

A further aspect concerns a method of identifying a source of data inputto a computing system comprising: receiving speech utterance from anentity related to randomly selected challenge text; processing thespeech utterance with the computing system to compute first acousticalcharacteristics of the entity; comparing the first acousticalcharacteristics with at least one reference set of acousticalcharacteristics for a human voice to identify a first score for thespeech utterance; comparing the first acoustical characteristics with atleast one reference set of acoustical characteristics for a computersynthesized voice in parallel to generate a second score for the speechutterance; generating a determination of whether the speech utteranceoriginated from a machine or a human based on the first score and thesecond score.

Some of the steps are preferably done by separate entities usingseparate computing machines, which can participate in an auction toidentify the first and/or second scores.

Another aspect concerns a method of identifying a source of data inputto a computing system comprising: selecting first test text data to bearticulated as a speech utterance by an entity providing input to thecomputing system; receiving the speech utterance from the entity;generating first recognized speech data from the speech utterancecorresponding to the first test text data; processing the firstrecognized speech data with the computing system to generate an initialdetermination of whether the speech utterance originated from a machineor a human; optionally repeating steps above based on a confidence scorefor the initial determination using second test text data, which secondtest text data is derived dynamically from content presented in thefirst recognized speech data; and processing the second recognizedspeech data with the computing system to generate a final determinationof whether the speech utterance originated from a machine or a human.

Another aspect is directed to a method of controlling access to acomputing system comprising: selecting first test text data to bearticulated as a first speech utterance by a first entity providinginput to the computing system; storing a voice print for the firstentity at the computing system based on the first speech utterance beingconverted into recognized speech data; wherein the first entity caninclude either a human or a computer using a synthesized voice;receiving a second speech utterance by a second entity; processing thesecond recognized speech data with the computing system to determinewhether the second speech utterance also originated from the firstentity; controlling whether the second entity is allowed to access anaccount and/or data based on comparing the voice print to the secondrecognized speech data.

The access is preferably used for one or more of the following:

-   -   a) establishing an online account; and/or    -   b) accessing an online account; and/or    -   c) establishing a universal online ID; and/or    -   d) accessing a universal online ID; and/or    -   e) sending email; and/or    -   f) accessing email; and/or    -   g) posting on a message board; and/or    -   h) posting on a web log; and/or    -   i) posting on a social network site page;    -   j) buying or selling on an auction site; and/or    -   k) posting a recommendation for an item/service; and/or    -   l) selecting an electronic ad.

Yet another aspect concerns a method of identifying a source of datainput to a computing system using prosodic elements of speechcomprising: presenting a challenge item to an entity, which challengeitem is associated with a reference set of words and associatedreference prosodic scores; receiving speech utterance from an entityrelated to the challenge item including an input set of words;processing the speech utterance with the computing system to computeinput prosodic scores of the input set of words; comparing the inputprosodic scores and the reference prosodic scores; generating adetermination of whether the speech utterance originated from a machineor a human based on the comparing.

Some preferred embodiments include the step: recognizing the input setof words to compute an additional prosodic score based on an identity ofthe input set of words, and comparing the additional prosodic words to asecond reference prosodic score related to a content of the referenceset of words. The challenge item is preferably supplemented with visualcues, the visual cues being adapted to induce the reference prosodicscores. The visual cues are preferably selected from a database ofvisual cues determined by reference to a database of human vocalizationsto most likely result in the reference prosodic scores.

Still another aspect involves a method of identifying a source of datainput to a computing system using prosodic elements of speechcomprising: presenting a challenge item to an entity, which challengeitem is associated with a reference set of words and associated prosodiccharacteristics; receiving speech utterance from an entity related tothe challenge item; wherein the reference set of words represents aselected set of one more contiguous words which when vocalized have ameasurable difference in prosodic characteristics between a referencehuman voice and a reference computer synthesized voice that exceeds atarget threshold; processing the speech utterance with the computingsystem to compute first prosodic characteristics of the entity;generating a determination of whether the speech utterance originatedfrom a machine or a human based on the processing.

Some preferred embodiments include the steps: estimating a firstcomputer synthesized voice that best correlates to the entity; andselecting the challenge item based on an identity of the first entity soas to maximize a difference in prosodic characteristics. Otherembodiments include further steps: soliciting utterances from aplurality of separate computing machines to determine their respectiveprosodic characteristics; and storing the plurality of associatedprosodic characteristics in a database of known computing entities.Multiple samples of individual challenge sentences are preferablycollected. The visual cues are preferably added to induce the entity tovocalize the reference set of words using the reference human voice.

Other aspects concern a method of implementing a CATPCHA (CompletelyAutomatic Public Turing Test To Tell Humans And Computers Apart) toidentify a source of data input to a computing system comprising:training the computing system with samples of human voices and computersynthesized voices articulating a set of reference challenge items;receiving a speech utterance from an entity related to one of the set ofreference challenge items; determining with the trained computer systemwhether the speech utterance was vocalized by a machine or a human.

A set of human test subjects preferably are used to identify whether areference challenge item was vocalized by a human or a computer prior tousing it in the training of the computing system. The referencechallenge items are preferably ranked and sorted according to a scoreprovided by the human test subjects, and further including a step:presenting the one of the set of reference challenge items based on thescore.

A further aspect concerns a method of implementing a CATPCHA (CompletelyAutomatic Public Turing Test To Tell Humans And Computers Apart) toidentify a source of data input to a computing system comprising:training the computing system with samples of human voices articulatinga set of reference challenge items; receiving a speech utterance from anentity related to one of the set of reference challenge items;determining with the trained computer system whether the speechutterance was vocalized by a machine or a human; wherein the computingsystem uses one or more speech models that are optimized for identifyinghumans using the set of reference challenge items.

A set of human test subjects preferably are used to identify whether areference challenge item was vocalized by a human or a computer prior tousing it in the training of the computing system. The set of referencechallenge items preferably represent a selected set of one morecontiguous words which when articulated have a difference in acousticalcharacteristics between a reference human voice and a reference computersynthesized voice that exceeds a target threshold as measured by areference group of human listeners, and at least some of the acousticalcharacteristics are used to train the one or more speech models.

Still another aspect is directed to a method embodied in a computerreadable medium for generating challenge data to be used for accessingdata and/or resources of an electronic computing system comprising:automatically generating a candidate challenge sentence from a first setof words and phrases using the computing system; automaticallygenerating at least one first utterance from a first machine text tospeech system for the candidate challenge sentence using the computingsystem, the at least one first utterance including first acousticalcharacteristics; automatically generating at least one second utterancefrom a human speaker for the candidate challenge sentence using thecomputing system, the at least one second utterance including secondacoustical characteristics; automatically determining a difference inthe first and second acoustical characteristics using the computingsystem to determine a challenge sentence acoustic score for thecandidate challenge sentence; automatically storing the at least onefirst utterance and the at least one second utterance and the candidatechallenge sentence in a challenge item database using the computingsystem for use by an utterance based challenge system when the candidatechallenge sentence acoustic score exceeds a target threshold.

Acoustical features of the first utterance and the second utterance arepreferably measured, and regions of greatest difference are identifiedto be used as discriminators. In other embodiments, differences inarticulation are preferably measured and scored on a diphone basis. Thediphones can be sorted according to their difference between human andmachine articulation. Based on such difference words and phrases arepreferably selected from a corpus of text in accordance with a diphonescore.

In some preferred embodiments a machine articulation statisticallanguage model is compiled based on a plurality of first utterances.

For other preferred embodiments a dialog of multiple challenge sentencesbased on questions and expected answers to be provided by an entity iscompiled. A challenge scenario preferably comprised of the candidatechallenge sentence and one or more visual and/or text cues is generated,which challenge scenario is stored in the database.

A natural language engine can also preferably process the words andphrases to generate the candidate challenge sentence. Anevaluation/scoring of the syntax difficulty preferably can be made forchallenge sentences to determine appropriate candidates that would bemore difficult for a challenge natural engine to decode.

In other preferred embodiments the candidate challenge sentence areannotated with prosodic elements to generate an expected prosodicpronunciation of the words and phrases. The candidate challenge sentencepreferably are annotated with first prosodic elements found in the atleast one first utterance and with second prosodic elements found in theleast one second utterance. A difference in the first and secondprosodic elements preferably is determined to generate a challengesentence prosodic score for the candidate challenge sentence.

In other preferred embodiments The candidate challenge sentencepreferably is annotated with first content elements for the visualand/or text cues found in the at least one first utterance and withsecond content elements for the visual and/or text cues found in theleast one second utterance. A difference in the first and second contentelements preferably is determined to generate a challenge sentencecontent score for the candidate challenge sentence.

In still other preferred embodiments a time required by the humanspeaker and a machine speaker to generate the first utterance ismeasured.

The challenge database can then be used during a processing of inputspeech by an entity to distinguish between a human and a machinesynthesized voice.

Another aspect concerns a method embodied in a computer readable mediumof selecting challenge data to be used for accessing data and/orresources of a computing system comprising: providing a first set ofdiphones using the computing system; generating an articulation scoreusing the computing system based on a machine text to speech (TTS)system articulation of each of the first set of diphones; and selectingchallenge text using the computing system to be used in an utterancebased challenge system based on the articulation scores. Thereafterspeech input by an entity using the challenge item database can beprocessed to distinguish between a human and a machine synthesizedvoice.

Another aspect concerns a method embodied in a computer readable mediumof selecting challenge data to be used for accessing data and/orresources of a computing system comprising: selecting a candidatechallenge item which can include text words and/or visual images;measuring first acoustical characteristics of a computer synthesizedutterance when articulating challenge content associated with thecandidate challenge item; measuring second acoustical characteristics ofa human utterance when articulating the challenge content; generating achallenge item score based on measuring a difference in the first andsecond acoustical characteristics; and designating the candidatechallenge item as a reference challenge item when the challenge itemscore exceeds a target threshold. Thereafter speech input by an entityusing the challenge item database can be processed to distinguishbetween a human and a machine synthesized voice.

In preferred embodiments the challenge item score is also based on oneor more topics associated with the text words and/or visual images andwhich are identified and measured in the computer synthesized utteranceand human utterance respectively. The challenge item score can also bebased on prosodic elements associated with the text words and/or visualimages and which are identified and measured in the computer synthesizedutterance and human utterance respectively. Alternatively or in additionto this, the challenge item score is also based on a collaborativefiltering score generated by measuring responses to a sequence of two ormore of the candidate challenge items identified in the in the computersynthesized utterances and human utterances respectively. Thecollaborative filtering score is preferably derived by identifying atleast a first reference correlation between at least a first referencetext descriptor for a first challenge item and a second reference textdescriptor for a second challenge item, including a probability that ahuman reviewer identifying the first reference text descriptor whenpresented with the first challenge item also provides the secondreference text descriptor when presented with the second challenge item,or vice-versa. Alternatively or in addition to this the collaborativefiltering score is derived by identifying at least a first referencecorrelation between a first challenge item presented in incomplete form,and a predicted response for completing the challenge item.

Still another aspect concerns a method embodied in a computer readablemedium of selecting challenge data to be used for accessing data and/orresources of a computing system comprising: defining a plurality ofdemographic groups, the demographic groups being based on age, sexand/or domicile; providing a plurality of CAPTCHA (Completely AutomaticPublic Turing Test To Tell Humans And Computers Apart) challenge itemsconsisting of a combination of images and solicited utterances with thecomputing system; for each of the challenge items using the computingsystem to compare a first reference response of a machine entity and asecond reference response provided by a representative of thedemographic group; for each demographic group selecting an optimal setof CAPTCHA challenge items determined by the computing system to yieldthe greatest response difference over the machine entity. Thereafterspeech input by an entity using the challenge item database can beprocessed to distinguish between a human and a machine synthesized voicebased on identifying a demographic group for an entity.

Other aspects of the invention concern a challenge apparatus or systemfor identifying a source of data input to a computing system comprisingone or more software routines implemented in a computer readable mediumand adapted to cause the challenge system to perform the aforementionedoperations o the various aspects described.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a general architecture of a preferred embodiment ofan articulated utterance based challenge system of the presentinvention;

FIG. 2 shows a more detailed block diagram of the main components of apreferred embodiment of an audible-based challenge system of the presentinvention;

FIG. 3 shows a detailed block diagram of the main components of apreferred embodiment of a content challenge compilation system of thepresent invention;

FIGS. 4A-4C depict various forms that can be used for presenting contentto an entity in accordance with the present invention;

FIG. 5 describes the basic steps required by an entity to decode contentand express an utterance related thereto;

FIG. 6 shows a conventional prior art CAPTCHA system based on a visualchallenge.

DETAILED DESCRIPTION Basic Elements & Concepts Employed in Invention

FIG. 1 depicts a typical computing system 100 environment which canbenefit from embodiments of the present invention, namely, in allowingflexible control over access to computing resources, data, etc., using aspoken language challenge approach. As can be seen in this figure, an“entity” (which may be a natural person or a machine) 101, 101′ providesinput to a data capture interface 110. In a preferred embodiment theinterface can be based within a GUI or a VUI, including within aweb/voice browser, a cell phone, a PDA, a desktop computing system, orsimilar electronic system including consumer electronic devices (such asset top boxes, cameras, conventional phones, etc.)

Again while the invention relies primarily on extracting audioinformation from the entity attempting access, other modes of data maybe provided at the same time through other mechanisms (i.e., throughkeyboards, mice, etc.). The invention therefore can be integrated aspart of a multi-modal input device. A Spoken Language Challenge System120 is responsible for receiving the input from the entity (i.e.,typically in the form of a short speech utterance) and determiningwhether it is a human or a machine. In most instances the challengelogic of the invention would be implemented as one or more softwareroutines executing on a server computing system. However the inventionis not restricted in this fashion, and those skilled in the art willappreciate that some components of the challenge logic could beimplemented advantageously on a client side as well or be embodied asfirmware/hardware depending on the platform.

Depending on the results of the Spoken Language Challenge System 120analysis, the entity may be granted access to one or more computingresources 130, which, again, may typically include such things as beingallowed to access resources/data of a computing device, access an onlinegame, set up an online account, access an online ID or account, accessan account through an interactive voice response system (such as by VXMLcoded pages), or access other types of data, such as email. For otherapplications it may be linked as a condition to: a) posting an item(electronic data or URL tags) on a message board, web log, an auctionsite, a content reviewing site (i.e., books, movies, etc.) etc., b)posting a recommendation for an item/service (i.e., as a recommendationsystem protector to reduce the effects of improper shilling attempts bythird parties; c) sending an email (i.e., a receiving email system mayinsist on a voice authentication to confirm that the message wascommunicated by a human, as a tool for reducing spam); d) selecting anelectronic ad presented by an advertising system—i.e., as a tool forreducing click fraud. These are but examples of course and otherapplications will be apparent to those skilled in the art.

Again the implementation will vary from application to applicationdepending on the particular needs of a system operator who desires toprevent/restrict automated machines from obtaining access to certainresources. In a typical example, as noted above, CAPTCHA systems aretypically employed to prevent entities from signing up for multiple freeemail accounts; those skilled in the art will appreciate that there arehundreds of other different applications. One critical aspect of allCAPTCHAs is that they must be easy to use by the majority of thepopulation, or they will simply frustrate users and reduce incentives toutilize a particular computing system. Because the large majority of thepopulation is able to read and articulate basic words, the presentinvention satisfies this criterion as well. In fact, for many cultures,it is likely that visual CAPTCHAs may be inefficient and/or unusable dueto inherent features or limitations of a native alphabet. Furthermore inmany cases persons may be visually impaired and unable to use a visualbased CAPTCHA. In this respect the present invention can complement suchexisting systems to provide a wider range of access for such persons.

In general terms, the Spoken Language Challenge System exploits otherfundamental strengths that humans have over machines at this time,namely: 1) the ability to rapidly recognize the meaning of a sentence ofwords; and 2) smoothly articulate the text of such sentence in the formof speech. Humans are trained over several years to understand propertiming, pitch, prosody, phoneme articulation, etc., andarticulate/pronounce words with ease. Machines are simply not ablepractically to duplicate this knowledge or reproduce such fluency.Consequently, as a basic premise it is submitted that a machine“impostor” will be detectable more easily by using human biometricinformation which must be expressed by an entity attempting to gainaccess to a computing system, not just simply as “understood” (as invisual and audio CAPTCHAs) by such entity and expressed in text form.The latter limitation of requiring an entity to respond only with text,or mouse clicks, fails to exploit one additional significant humanprocessing/expressive feature which is extremely difficult (if notimpossible) to duplicate.

For example, as shown in FIG. 5, the basic functional steps performed bya human reader and speaker are shown in an articulation process 500.These tasks include the following: identifying the words/text of asentence at 510; understanding a meaning of the sentence at 520;evaluating prosodic aspects of the sentence at 530; and finally at 540articulating the text of the sentence with correct pronunciation andappropriate prosody.

Currently conventional computing systems can duplicate such tasks, butonly with noticeable/detectable side effects. That is, an opticalscanner routine and conventional natural language understanding systemcan perform steps 510-530 with reasonable performance, but not nearly asquickly as a human. More importantly, in performing step 540 a text tospeech (TTS) system can articulate the result, but with very noticeableresults. Thus while they are able to perform all of these operations insome fashion, they are unable to perform them all in seriatim inreal-time to duplicate a human.

For instance suppose one or more of the following sentences arepresented to an entity within an interface accompanied by a request tovocalize the same as an input to a challenge system:

I did not permit him to go outside!

I need a permit for my construction.

In doing the same tasks in FIG. 5, a machine imposter must first “see”the text presented (step 510). This task in itself would require someform of optical scanner and character recognition to identify distinctwords. The conventional CAPTCHAs, as noted above, in fact rely primarilyon being able to fool machines by rendering such text unrecognizable.However, it can be seen that unlike the present invention, such approachonly takes advantage of a small fraction of the processing battle whichcan be imposed on a machine impostor.

After identifying the raw text, the machine imposter must then parse theentire sentence to make sense of the meaning of the phrases (step 520).This, again, typically requires a natural language engine (NLE) whichmust be fairly advanced and most often “trained” for particular domainsto understand pre-defined phrases from a known grammar. In a completelyunstructured context devoid of additional cues, a NLE would be impairedand confused trying to “understand” a random sentence ofwords—particularly sequences which may be designed to exploit frailtiesin such systems. For example in the sentences above, the system wouldhave to detect very quickly whether the word “permit” was being used asa verb or a noun. This must be identified correctly, of course, toinform a TTS engine of the correct pronunciation. It can be seen thatevent random text analysis by a NLE without context is extremelychallenging, because several aspects of the sentence must be processedvery rapidly to perform word/sentence segmentation, abbreviationexpansion, numeral expansion, word pronunciation, and homographdisambiguation.

After understanding the sentence, the machine imposter may also have toannotate the output of the desired articulation with appropriateprosodic elements at step 530. Acoustical aspects of prosodic structurealso include modulation of fundamental frequency (FO), energy, relativetiming of phonetic segments and pauses, and phonetic reduction ormodification. For example a phoneme may have a variable duration whichis highly dependent on context, such as preceding and followingphonemes, phrase boundaries, word stress, phrase boundaries, etc.

For the first sentence therefore there may be different prosodies oremphases that can be appropriately placed on different words. Forexample, depending on the context, any of the bolded words might beemphasized by a speaker to give a more precise meaning:

I(1) (in the context of who gave permission)

did not(2) (in the context of affirmation/confirmation)

permit him (3) (in the context of who was given permission)

to go outside! (4) (in the context of location)

Others are of course possible to give different meanings. To reinforce adesired meaning and accompanying prosody, the sentence can be presentedwith visual clues or other sentences to assist the speaker indetermining which context is appropriate. Nonetheless as is apparentfrom the ambiguity of the sentence, it may not be necessary to place toomuch emphasis on particular prosody or emphasis given by the entity. Asa first layer of defense or threshold it may be sufficient to simplydetect if at least one reasonable choice is given as a response for thecontext in question—i.e., that prosody was indeed used in expressing thesentence. The prosody choice may also be tied to a prior statement, suchas: Did you say he could go outside? No, I told him he could go to hisroom. In the context of location being emphasized in the question, theanswer should similarly give prosodic emphasis to that same element.

Note that in many cases prosody can be determined solely by reference toacoustical characteristics of the speaker, and without requiring therecognition of words. In some instances it may be desirable nonethelessto compute an additional prosody score based on an identity of thearticulated words, and comparing said it to a reference prosodic scorerelated to a content of the overall challenge item set of words.

However step 540 presents the biggest challenge to the machine imposter.To imitate such behavior, a TTS system must know the phonemes to beuttered, which words to accent (i.e., emphasize), and how tointelligently process the sentence to imitate prosodic behavior of ahuman. Again for each phoneme, the TTS system must decide on the timebehavior of the articulation—i.e., duration, intensity stress andintonation, all of which can change the meaning of a statement entirely.Thus the TTS system must be given accurate information by the NL engine.For example in the first sentences above the system would need to knowto accent the last syllable of the word “permit” and the second syllablein the second sentence to provide the accurate syntax.

Finally, TTS systems also have significant problems in the actualarticulation of speech as noted in step 540. Typically pitch range isrestrained controlled because a TTS system may be unsure of how toproperly adjust pitch, stress, etc. for a particular phoneme/word. Thisgives the speech a pronounced and detectable mechanical sound.

Modern TTS systems rely primarily on concatenative synthesis—a techniquewhich relies on extracting model parameters from actual speech waveformsand concatenating them individually to create new utterances. Parts ofutterances that have not been previously processed and stored in thedatabase are constructed from smaller units.

The challenges for this technique include the fact that it iscomputationally extremely complex to determine which waveform “unit” toselect in any particular instance, and then how to modify the same to beprosodically correct (i.e., desired pitch, duration, intonation, etc.)as noted above. Other techniques have similar limitations.

To address coarticulation, diphones—representing transitions betweenphones—are typically used. In the diphone approach, all possiblediphones in a particular language are stored, and then they are mergedto correspond to the phonetization of the input text. A diphone again isa speech unit consisting of two half-phonemes, or of the phonetictransition in between, e.g. “Cat”: silence+c−c+a−a+t−t+silence

Another method, unit selection, collects its speech data from adepository containing units of various lengths, including diphones aswell as words and phrases. Each pre-recording is stored in multipleoccurrences, pronounced in different prosodic contexts. This type ofsynthesis requires an extensive storage facility, and has only recentlybecome a popular method, since memories and performance of computershave increased.

From perusing any of a number of speech synthesis sites, including thoseassociated with the most advanced speech synthesis engines (AT&T, IBM,etc.) it is apparent that clipping by TTS systems is very noticeable.That is, transitions between phonemes are often accompanied by sharpdistinguishable breaks. Consequently current machine impostersattempting to imitate a human voice merely try to model the humanbrain's understanding of the parts of speech of a sentence, and makestatistical guesses about the proper pronunciation of words. Real-timemodeling of phonemes and stress placement are (essentially) impossibletasks at this time.

The present invention is based on the hypothesis that given thecomplexities of language, a computer will not be able to imitate a humanin a manner that will not be detectable—at least not in a way that isnot detectable by another computer trained to “listen” for machineimpostors. In this latter respect, therefore, while a TTS system mayeventually reach “human-like” performance, it will always include smalldeficiencies, artifacts and tell-tale signature signs of speechsynthesis operations which will be observable and measurable byconventional speech recognition systems of equal or better computingcapability.

Accordingly the present invention exploits the fundamental premise thatspeech synthesis systems will invariably lag in function, performance,capability, etc., as compared to speech recognition systems, simplybecause of the relative complexities of the tasks which systems mustperform. The latter has the advantage that it can be trained extensivelywith millions of examples of human speech, as well as computer speech,to very quickly, easily and accurately differentiate a human from amachine. So in a sense it can be said that the best mechanism forcatching a machine impostor is by using another machine as a detector,since the speech “decoding” process is inherently capable of detectingthose aspects of a speech “encoding” process which identify a machinesynthesized articulation.

In summary, TTS systems at best can merely try to model the humanbrain's understanding of the parts of speech of a sentence, and makestatistical guesses about the proper pronunciation of words.Consequently they will always suffer to some extent from one or more ofthe following deficiencies:

Pauses

Misplaced stress of phonemes

Misplaced stress of words

Discontinuities between phonemes, syllables

Incorrect prosody

All of which can be detected by a spoken language challenge system. Itwill be understood, of course, that a spoken language challenge system120 may not be appropriate for all applications, because they may lackor have limited means for an audio input. Nonetheless, for someapplications, particularly in portable systems (such as cell phones,PDAs, regular phones) where there is very little (or no) physical spacein a display (or inadequate resolution) for presenting a visual CAPTCHA,it is expected to be far more useful than conventional approaches.Moreover, in some instances, such as IVR systems, the present inventionis one of only a few known mechanisms for preventing unauthorized entryby machine impostors.

In some applications a system operator may want to use or even combinedifferent forms of CAPTCHAs for extra security so the present inventioncould be used to supplement conventional visual and audio basedtechniques. For example, both visual and audible CAPTCHAs could be usedin a hybrid challenge system.

An entity could be presented simultaneously with a number of visualdistinct word CAPTCHAs arranged in the form of a sentence. The entity isthen required to read an entire sentence of words that are each visuallyconfounded, thus increasing the chances that a computing system willfail to process such data in a reasonable time frame.

Speech Verification System for Detecting “Liveness” of Human User

Having considered the limitations of machine imposters, an explanationof a preferred embodiment of an audible-based challenge system is nowdisclosed. In a first preferred embodiment 200 shown generally in FIG. 2advantage is taken of the fact that the human vocal tract is a highlycomplex acoustic-mechanical filter that transforms air pressure pulsesinto recognizable sounds in a manner that is not duplicatable (at thistime) by a machine. Thus this embodiment exploits the difference betweena conventional TTS loudspeaker based articulation, and a human vocaltrack articulation.

The architecture of the system is as follows: a spoken utterance (from ahuman or a machine) is captured by a routine 210. The types of routinessuitable for capturing speech data from within a browser, or from a cellphone, are explained in detail in U.S. Pat. Nos. 6,615,172, 5,960,399and 5,956,683 (Qualccomm) respectively. The current ETSI Aurora standard(ES 201 108 Ver. 1.1.3) available at an http site atportal(dot)etsi(dot)org/stq/kta/DSR/dsr.asp also describes such processand is incorporated by reference herein.

In conventional speaker verification systems, speaker identities aretypically confirmed using a biometric evaluation of the speaker'sarticulation, which includes measuring such human traits as lungcapacity, nasal passages and larynx size and storing these as physicalbiometric parameters within a template. A person's tongue can alsoinfluence their articulations. This same technique can be exploitedhere. Thus a speech utterance is captured, and selected acousticfeatures are extracted by a routine 210 which best correspond with—andidentify—the particular biometric parameters unique to that person.Thus, it is well accepted that the audio spectrum of a voiced sound froma person inherently carries with it sufficient information to uniquelyidentify a vocal tract of such person and act as a biometricfingerprint.

The utterance is presented in response to a sentenceselection/presentation logic routine 220, which is responsible forproviding the user with appropriate content 221 to articulate. Forexample, the system may ask the user to speak the following sentence:

This is How We Recognize Speech

Other choices for the content to be articulated are discussed below inconnection with FIGS. 3 and 4A-4C. The challenge item/content 221 may infact be articulated by the challenge system with a request that theentity seeking access repeat the content. In other embodiments it willbe desirable to overlay the present system with a conventional visualbased system, so that, for example, the sentence above is shown as asequential set of visually distorted words. By carefully selectingcontent for the verification based on known machine weaknesses theaccuracy of the system in distinguishing between humans and machineimposters can be increased.

The appropriate acoustic features of the user utterance are thencompared by a routine 230 to determine a best match HPn against a voiceprint 250 of a known human speaker. This aspect of the invention, sofar, can simply be based on a conventional speaker verification system(such as one currently offered by Convergys) to perform one phase of theverification. A company known as voiceverified offers a similar solutionat www(dot)voiceverified(dot)com.

The present invention goes beyond that, however, to cover instanceswhere the speaker is unknown, but should still be given access. If thespeaker is unknown, or if speed is a consideration, one alternative fora faster determination is to determine a distance to a nominal humanvoice print reference 280. Based on these results, the speaker may beeither expressly identified (in the case of a human match) or onlytentatively classified (in the case of no existing match) as a humanspeaker.

The other phase of the comparison simply modifies the prior artarchitecture to compare the unknown speaker utterance extracted featureswith a routine 231 against known machine speaker templates 251. Again ifthe speaker is unknown, or for speed purposes, one alternative for afaster determination is to determine a distance to a nominal machinevoice print reference 280. Based on these results, the speaker may beeither expressly identified (in the case of a machine match) or onlytentatively classified (in the case of no existing match) as a machinespeaker.

In a final evaluation, a comparison is made by a routine 260 against theclosest human match HPn and the closest machine MPn to make adetermination on whether the speaker is human or not. Based on theresults of this algorithm, which may be implemented in any number ofways known in the art, a decision/output is provided at 261 to allow ordeny access based on such confirmation. For some applications it may bedesirable to further include a time element in the determination, so,that, for example, a user is given a certain period of time in which toarticulate or identify the challenge item/text. The amount of time canbe based on thresholds established from real world examples so that ahuman speaker is expected within a certain confidence level to return aresponse. If the speaker does not articulate the sentence within thisperiod this factor may be used to weight the evaluation or in fact useit as an outright rejection depending on system requirements andspecifications.

Note that in those cases where only a reference print is used, thisaspect of the invention modifies the prior art system to discriminatenot between individual users, but, rather, to determine simply if anentity providing an utterance has one or more measured vocal tractcharacteristics above certain thresholds.

The two verification routines can also be done in parallel, on differentcomputing platforms adapted and optimized for each type of verification.Thus, for example, a first provider may be employed to determine a voiceprint of an utterance based on an ISP or other profile information forthe user associated with the request. It is possible, for example, thatcertain population groups would be better identified by reference to adatabase of voice prints 250 unique to a certain onlinecommunity/population. This would be faster than attempting tostore/access a large database online of several million potentialmatches.

Some entities may have particular expertise in resolving/identifyinghuman voices based on their expertise in classifying such voice prints.The invention contemplates scenarios, therefore, in which a machinedecision criteria routine 240 may determine (based on a cost parameter,an accuracy parameter, a speed parameter, etc.) to allocate the task ofclassification to one or more verification providers (not shown) who inturn would each have their own voice print databases, verificationlogic, etc. Incentives could be provided to those entities respondingmore quickly, or more accurately, or with less cost.

In other cases it may be desirable to conduct an auction for the rightto resolve the verification question. For such situations, averification entity may opt to bid on specific types of utterances,depending on the language of the utterance, the purported identity ofthe user, the country of origin of the speaker, the type of sentenceselection logic used, and so on. The bid may be in the form of apositive or negative credit, so that in some instances the verificationentities have to pay a certain rate for the right to classify thespeaker, while in others they may get a credit of some sort forperforming such task.

All of the above would apply equally well to the machine voice printverification process. It is expected that different entities, over time,will develop unique libraries of machine voice prints that can used forsuch process. In some embodiments it may be desirable, therefore, tosubmit the task to multiple verifiers to ensure a more accurateresponse.

Techniques for creating libraries/databases of voice prints 250, 251 arewell known. These systems are able to create digital voice “prints” 250capturing the unique physical characteristics of a speaker's vocaltract. The set of voice prints 250 are created using standard enrollmentprocedures, which can be duplicated in embodiments of the presentinvention as well to create new voice prints if desired. For example inbanking services, the user is prompted later to repeat a certain phraseor password to gain access to an account. Descriptions of such systemscan be found in U.S. Pat. No. 6,356,868; and U.S. publication no.25096906; and WO005020208 all of which are incorporated by referenceherein.

Similarly speech processors which can create voice templates fromacoustic features, and verify human identities from their vocal patternsare well-known in the art; for example systems by Scansoft (SpeechSecure) and Nuance (Verifier) can perform this function. Theaforementioned WO005020208 application uses a different form ofvoiceprint which does not use spectral features, but is claimed to bemore compact and efficient because it employs only sets of rationalnumbers which are invariant from utterance to utterance. Other suitableexamples will be apparent to those skilled in the art.

Consequently, it is relatively simple to obtain a number of actualsamples from live persons, with different enrollment words/phrases tostore as reference voice templates 250, 251, and to generate referencevoice templates 280, 281. Because most voice verification systems aretext dependent, it is preferable that the number of enrollmentwords/phrases be relatively large, and augmented or changed on a regularbasis, to make it difficult for a machine to “learn” any of theavailable choices. In any event, at the end of the verification process,the information from the speaker utterance is used to update ahuman/machine voice print databases 270, 271 respectively as needed.

To identify a closest voice print match, the human language comparator230 and machine articulation comparator 231 may use a distance betweenvectors of voice features to compare how close they are to a particularperson/machine. For example, the integral of the difference between twospectra on a log magnitude may be computed. Alternatively the differencein the spectral slopes can be compared. Other examples will be apparentto those skilled in the art.

In the absences of access to a library of voice prints, and totrain/bootstrap these systems, it may be appropriate to create the humanvoice print reference 280 (and machine counterpart 281) by normalizingacross a large sample to create a set of average pseudo-or human proxyrepresentative vectors. The new sample utterance is then comparedagainst such reference.

As seen in FIG. 2, the speaker is presented with a challenge item (inthis instance text) to articulate. In other cases, the computer may askthe speaker to repeat a sentence as part of the challenge item content.The types of spoken challenge items preferably used in the presentinvention are described below with reference to FIGS. 3 and 4A-4C.

In some implementations the speaker may be asked to change theirposition relative to the microphone, such as by placing it closer ornearer in order to better characterize the acoustic features.Consequently a series of articulated challenges at different positionscould be used to assess the nature of the speaker.

It can be seen that the preferred embodiment treats all humansessentially as one class of potential users of a computing system.Similarly, all machines are classified within a second class which isalways excluded. Nonetheless it may be desirable in some embodiments tohave humans which are excluded, and conversely machines which areexpressly included. For example some environments may call for onlyauthorized machines to participate.

Thus for some embodiments it may be desirable to use the system toexclude even human participants who are detected and matched against ahuman voice print associated with a person who is to be denied access.This would be beneficial in many instances for setting up accounts, toprevent users from setting up multiple accounts under differentpseudonyms, such as is the common practice in online message boardsystems.

It may be advantageous to see that certain users should be excluded,even if they are human, due to undesirable behavior in otherapplications (i.e., duplicate accounts, spamming, terms of serviceviolations, etc.) Such persons could be “locked out” by requiring themto sign in with their voice print when they attempt access. Since thesystem will “recognize” their voice by comparing to the prior storedtemplate, the user can be denied access in this fashion.

Consequently in this variant of the invention even if a particular“authorized” user attempts to re-enroll in a different session with adifferent enrollment phrase/password, the present invention canimplement the type of system described in Magee—U.S. Publication No.2005/0125226 incorporated by reference herein, in which voice prints ina database are continually compared against each other for detectingfraud. If a “match” is determined within certain controllablethresholds, the user can be rejected from further access, or haveprivileges restricted or removed from any new account. This techniquewould is allow detecting machine impostors as well attempting to createmultiple personalities/accounts.

This variant of the invention is particularly useful in the area of webblogs, where spammers are now hiring large numbers of persons in thirdworld countries to solve conventional CAPTCHAs implemented at blogsites. The intruders then post large number of spam posts which areundesirable. By using the present invention, a blog site could implementa standard one sentence enrollment test, on a case by case basis,depending on the origin of the enrollee (i.e., a standard CAPTCHA may beused for the remainder of users). After the enrollee “passes” the firsttest (i.e., he/she is not a machine) the voice print is stored. At alater time, if the same enrollee attempts to create a second account(for the purpose of creating more spam) the system would identifyhis/her voice, and prohibit such action, because it is difficult toalter one's voice consistently for the same challenge phrase.Alternatively, even if the enrollee is not immediately locked out, it ispossible to simply disable access for the second account at a later timeafter detecting offline that the voice matches to a prior template.

Similarly the invention could be used to “differentiate” betweennationalities, because foreigners speaking English tend to havepronounced accents and similar lack of correct prosody because they arenot reared in the particular culture to understand certain nuances oflanguage. Specific reference models could be created on a country bycountry/culture-by-culture basis. For example the system could betrained to distinguish between far east Indian English speaking personsand Hong Kong English speaking persons. Moreover many of them may haveno English fluency whatsoever, so unlike the prior art which reliessolely on a keyboard entry, imposing a spoken language test wouldeliminate such persons as potential intruders, because they could bedetected by a number of the same tests imposed on machines noted above.Consequently the invention could be used to automatically lock out (ortemporarily quarantine) new accounts originating from users of a certainregion of the world which is demonstrated to be primarily spam driven,and further determined to be not reasonably likely prospective audienceparticipants of a particular blog.

Thus the above methods could be easily employed on an IP address-by-IPaddress basis, so that only certain known foreign jurisdictions with ahistory of abusing the system may be scrutinized by the system to detectduplicate accounts. To comply with applicable laws concerning voicerecordings the user could be presented with a waiver form, and/or thevoice data could be actually “recorded” as a voice print in a computingsystem located in another jurisdiction.

Similarly, for the second class of cohorts, typically a challenge systemwill always deny access to machines. Nonetheless, it may be desirable insome instances to conditionally allow (or only allow) “authorized”machines to obtain access to computing resources, account services, etc.For example a challenge system may offer the entity an opportunity topresent an authorization code as part of the process. If the machine canarticulate the appropriate passcode, the challenge system may permitaccess under any appropriate or desirable terms. This may be useful, forexample, when a person may want an electronic agent to accessinformation, accounts, etc., and perform transactions on their behalf.In such cases the machine prints are not used to exclude, but rather toconfirm the identity of the electronic agent as would be done with aconventional speaker verification system for human speakers.

Spoken Content Challenge Routine

Of course in any system that is designed to discriminate against acomputer imposter one must deal with the inevitable compensation schemeswhich could be developed to counter the test being used as thedeterminant. Since the present invention is based on the notion that acomputer imposter will not be able to accurately pronounce a randomlyselected set of phones, it is useful to consider a few other parametersthat can be adjusted to reduce the likelihood of an intruder “beating”the system, such as with a brute force scheme.

As noted above in some instances it may be desirable to discriminatebetween a human and a machine solely based on differences in acousticalcharacteristics in the articulated speech. In other words, withoutrequiring any actual speech “recognition” per se to detect the contentof the utterance for its correctness. For other applications it may bedesirable nonetheless to detect that the articulated words (or an image)are indeed “correct” for the text in question, or for the challenge itempresented as noted below.

From a basic perspective it is generally accepted that there are least40 separate English phones, or separate sounds used to articulate words.This means that for a word that contains n phones the number ofpotential permutations of phones is approximately ₄₀P_(n) which is equalto 40!/n! which can be extremely large. Of course it may be easy tosimply store a reasonably human facsimile of such phones for all actualEnglish words which is much smaller. However this would have poorperformance on transitions between phones compared to a diphone system.Moreover as noted above, the task of picking out the appropriate phoneis complex as it depends on syntax, prosody, etc., so this can be usedto gain an advantage over an imposter.

The more advanced TTS systems may use diphones, which are slightly moreaccurate for reproducing speech. Generally speaking diphones arecombinations of phones, which means that there are approximately 1600 ofsuch in the English language. Nonetheless there are probably only200-300 “in use” diphones within the grammar of English words. Even withthat limitation, however, it can be seen that the for a simple sentenceof only a few words that contains N contiguous diphones, the number ofunique sounds to be reproduced is at a minimum 20*10^(N). Thus, evenwhen N is only in the range of 10 or higher, this type of articulationchallenge very rapidly moves out of the realm of a brute force solution.

Moreover there may be some diphones which, due to their nature, are muchmore difficult to reproduce, and thus more susceptible to detection.These can be experimentally identified as noted below, and exploited toimpair a machine intruder.

Accordingly sentence selection/presentation logic 220 is another aspectof the invention which can be tailored specifically to exploit machineweaknesses and human strengths. To wit, this routine is responsible forselecting and presenting the content to the utterance speaker. Bycareful selection of such content, the chances of detecting a humanversus a machine can be increased dramatically.

A more detailed diagram of the basic architecture for a contentselection challenge routine 320 is shown in FIG. 3. The main componentin this instance is a content (in a preferred embodiment a sentence)synthesis engine (CSE) 310, which receives a variety of data inputs anduses a variety of rules and scenarios to output/construct appropriatecontent challenge items which can be used by a content challenge routine220 (FIG. 2). These candidate sentences are tested against machine andhuman trainers to develop a suitable body of challenge sentences in adatabase 395.

One body of information which the CSE routine draws upon is adictionary—grammar 315. The latter is preferably populated by an entireset of conventional words, phonemes, phrases, etc. for a particularlanguage, as well as a database of homographs, built from a corpus 314.Homographs are words which are spelled the same, but which havedifferent pronunciations. A human reading a sentence with a homograph(like the words “read,” “close” and the like) can be expected to readilyperceive the correct context of a word, while a computing machine islikely to require additional time to understand the syntax of asentence. This time factor, again, can be exploited to differentiate andscore the utterance. Thus one basic strategy would be to employsentences with homographs to handicap a machine intruder.

The CSE engine 310 also has access to natural language rules 316,prosody rules 317, coarticulation/concatenation rules 318 and a scenariobuilder 319. These reference rules help the CSE construct appropriatechallenge sentences and acceptable pronunciations based on theguidelines noted above.

For example to develop a set of content challenge items 395, a varietyof sources and strategies could be used. In a first technique, onestarts off with a reference corpus 314 of raw prose from any of a numberof sources (newspapers, books, Internet, etc) to develop a set ofevaluation sentences having a word form W1+W2+W3+ . . . Wk and anassociated diphone sequence: {P1 a, P1 b, P1 c} {P2 a, P2 b} {P3 a, P3b, P3 c} + . . . {Pka} and so on. Each of these are then processed withapplicable rules 316, 317, 318, etc. to form a candidate sentence 311that is then evaluated according to various criteria (elaborated below)to determine if it is a useful and suitable discriminator between a livehuman and a machine imposter. If it turns out to be an appropriatechoice it is placed into challenge item set database 395, and presentedas one of the options for a challenge item as seen in FIG. 4A in theform of explicit text. As noted earlier, for additional security it maybe desirable to visually scramble the words using a visual CAPTCHAtechnique first, and then present the same for articulation. In thishybrid scenario, both acoustic and content features are measured toincrease the difficulty of the challenge item for a computing system. Tofurther increase the duration of the challenge (and hence make itunattractive to automated systems) the visual images (which couldinclude depictions of objects and not just distorted text) could bepresented in a deliberately paced manner so as to reveal the detailsover time. Thus the full image would not be revealed until 30 seconds,but would be improved gradually with additional visual details duringsuccessive iterations every 4 or 5 seconds. The system could take intoaccount the time it takes an average person to provide the correctanswer for the gradually revealed image as part of the scoring.

Referring again to FIG. 3, a second technique would involve workingbackwards, by identifying and compiling a list of diphones andconcatenations in a database 396 which are known confounders for TTSsystems. From this set of confounders a set of candidate sentences couldbe automatically constructed or located in a dictionary/grammar 315. Inessence, the strategy here is to measure a set of concatenationgaps/discrepancies for each diphones. Based on developing and sortingsuch list to identify a preferred set of distinctive diphones 396 thatare most likely to confound a TTS system, the system selects sentencesfrom the corpus containing multiple confounding phones, or constructsthem from scratch as noted. When constructing sentences from scratch itmay be desirable in some cases to use some random word sequences andwithout considering syntax rules. Thus the end result should be a set ofrandom sentences constructed from word/phoneme patterns which areextremely difficult (i.e. detectable) for a machine to imitate in humanform.

A third technique would combine the challenge sentence with a visual cueof some sort to induce a prosodic response. These can be determinedexperimentally with reference to a human population by giving anassortment of sentences and visual cues and measuring a response givento the challenge sentence. An example is given in FIG. 4B in the form ofa question that the speaker must respond to, and preferably provide ananswer that falls into a category of acceptable responses. Thus thechallenge item set database 395 would include such additional cue dataas well.

The visual cues could request information from the speaker such as shownin FIG. 4B, along with other cues if necessary, such as text thatexplicitly reads:

He's hitting a ball

He's fishing

He's driving a car

He's reading a book

He kicked the cat

etc., to help give a roadmap to a human speaker.

Note, too that the visual cues can be tested with a population of humansto determine which ones are most likely to induce a preferred set ofreference prosodic characteristics. Since it is desirable to exploitthis difference over machines, it would be useful to identify thosevisual cues that result in a maximal prosodic difference over areference computer synthesized voice and store these in a database alongwith their prosodic scores. These examples can be easily determined withroutine experimentation, and can be tailored if desired to a detectedgender/demographic of the entity attempting access. Thus the challengeitems and associated cues can be customized within the database withfields pertaining to a particular estimated audience that is attemptingaccess. The initial determination of gender, age, demographics, etc.,can be done using any number of known techniques.

A fourth technique would rely on a lack of any explicit text cues.Instead it would offer up a set of pictures/images/cartoons with aprompt for the speaker to give a free form response. The responses arethen tabulated and correlated to identify a set of thresholdsidentifying an acceptable response. For example, in response to pictureA, the set of responses included X—50%, Y—35%, Z—5%, other 10% and soon. A statistical profile can then be used against an entity attemptingaccess to see if the response matches one of the human provided answersin the reference set. As before, the images can be tested and optimizedby reference to prosodic scores as well so that a database of preferredchallenge items is developed.

An example of this is shown with reference to FIG. 4C. The challengeitem may be in the form of a bubble reader, that asks an open endedquestion like “what did this person say” or “what is this person doing”?etc. Specific suggestions can be given to the speaker, as well, toinduce an accurate human response. Other basic questions might includefacts which the speaker will know without reference to another source,such as:

-   *what is your full name?-   *describe for me what color your clothes are-   *tell me what you ate for breakfast?-   *tell me all the good things that come to mind when you think about    your mother?

The example sets can be augmented with collaborative filteringtechnology as well, so that correlations can be developed between humanson appropriate responses to challenge items. Thus, if two humansresponded with the same answers to multiple challenge items, theseindividuals' profiles can then be compared to an unknown speaker topresent yet another form of human identity detection. For example, if alarge percentage of persons who see X say A in response, and suchpersons also, upon seeing Y say B in response, than a content challengeitem can be presented to show both X and Y to detect the samecorrelation in responses. Of course such test may not determinative butit can act as a further overlay in the access determination along withthe required correct pronunciation of the words in question.

Along the same lines the challenge item could be presented in the formof a game or puzzle in which an image or a set of words could bepresented to the entity with a request to predict or guess the next itemin sequence. The items could be selected, again, based on a Bayesiantype analysis of actual interviews with humans which solicit specificresponses based on human associations. For example in the colorsequence:

-   Green-   Yellow-   (please fill in the blank)    A very common choice would be red, representing the colors on a    traffic light. Other examples for other applications will be    immediately apparent to those skilled in the art.

Similar evaluations of prosody scores can be made to identify prosodiccharacteristics of particular challenge items. By identifying andranking common prosodic elements presented by humans in articulatedsentences, the system can use such scores to compare against an unknownentity for prosodic (dis) similarities.

As noted, scenario builder 319 is the entity which is responsible forcompiling appropriate qualified content to be evaluated, as well as tocreate the pictorial challenge item sets shown in FIG. 4B and 4C. Thiscan be done by reference to a library of images/sequences, referencefacts, context rules, question and answer sets, etc. To determine thisset a supporting routine may examine logical explicit tags provided byhumans on the Internet for image data and the like. For example awebsite or search engine may contain an image with the tags Cat, Petprovided by human taggers for a picture of a household cat. This taggingdata then provides a reasonable starting point for which to provide theexplicit cues in the aforementioned figures along with the challengeitem text.

In other instances the scenario builder may suggest a sequence ofquestion/answer sets to be used for the challenge item content, so theaccessing entity is effectively engaged in an interactive dialogsession. In this manner the timeliness and naturalness of the responsescould be evaluated over a series of multiple questions. Simple questionssuch as “tell me what you are wearing” would get a first answer, and afollow-up could be built dynamically—based on the detection that theentity said “ . . . a shirt”—to ask what color the garment is, and soon. An example of another question would be “What movie have you seenrecently.” If the answer was “Star Wars,” for example, the system couldfollow up with “who played Darth Vader” or “who was your favoritecharacter,” or some other pre-stored question/answer associated with themovie, subject to the limitations of maintaining a database of suitablequestions and answers. Similar questions could be posed for books,music, sporting events, job occupations, etc., Again the questions andcontent could be randomly selected and based on a selection of contentthat is known to present challenges for a machine. The user could beprompted to pick which 1 of a series of topics he/she would like to talkabout. Thus a series of Q/A scenarios could be designed and presented tobetter weed out improper entities, since the likelihood of detectionwould increase with each utterance.

Looking again at FIG. 3 the candidate sentences 311 can be presented toone or both of a machine template trainer 330 or a human templatetrainer 340. These routines can be embodied in separate computingsystems which are adapted to measure and catalog an individualperson/machine's articulation of the candidate sentence from a group ofsuch entities. Thus each is responsible for compiling a set of voiceprints representing a group set of reference articulations for thecandidate sentence. For example, a set of M different machine TTSsystems could be presented with the challenge of articulating thecandidate sentence 311, and their responses catalogued and stored. Thiswould aid, for example, in developing a library of known TTS cohortsevaluated with respect to the candidate sentence. Later, during averification process, an initial determination can be made as to whichcomputing entity best correlates to a particular utterance. Based onsuch determination a challenge sentence or text can be selected thatbest discriminates for such machine.

Similarly a group of N different persons with different demographicscould be tested in the same way to develop a set of voice prints for thesentence in question. In a preferred embodiment the sentences could bepresented in connection with an online gaming site, so that thearticulator data is easily collected from a wide population base andwithout having to resort to specialized groups or expense.

Routine 350 is then responsible for measuring the individual differencesbetween each of the N human audible responses and the M machine audibleresponses. As noted these differences can be calculated in any number ofways known in the art. This can be compiled in the form of atable/matrix to determine those candidate sentences offering thegreatest promise in terms of their ability to differentiate betweenhumans and machines because the distance for such candidates is thegreatest. Thus, for example, a threshold evaluation routine 360 may beset to choose only those candidate sentences which represent the highestX % difference in human vs. machine renderings. For example, the top 5%or 10% may be selected to become part of the challenge item set database395. The exact criteria will vary according to each application ofcourse according to the desired speed/accuracy/security requirementsimposed.

As a supplement to the scoring routine 350 could present the candidatesentence articulations to a group of human evaluator/raters to collecttheir vote or opinion on the liveness of the entity (human or machine)responsible for the utterance. The numbers and percentages ofacceptance/rejection can be tallied for each candidate sentence asarticulated by the M machines. This rejection factor can be used as wellfor systems which may simply employ a non-automated challengeevaluation, or as an initial filter for reducing a set of candidates forthe final challenge item set database. That is, an initial set ofcandidate sentences may be filtered by a group of human observers, andthe ones which nonetheless least human like could be then subjected tomore extensive processing to identify more precisely the acousticdifferences resulting in the non-human signature. This examination canbe done for each machine entity to develop a library of known acousticaldifferences for different candidate challenge items. This way, after aninitial determination is made as to the likely identify of an entity, arandomly selected challenge item can be presented to each that isnonetheless designed to exploit the largest known (significant)deviation from a reference human voice.

In those situations where a candidate sentence is considered appropriatefor the challenge item set, the individual human voice print databaseand machine voice print databases can be updated. Again, as noted above,it may be useful to compile a simple reference template representingeach collective entity (i.e., HPr 280, MPr 281) as noted above usingaggregate statistics for the entities measured. A notation can be madeby a routine 385 to note that the phones/words in question resulted in auseful candidate, and this information again can be used as seedinformation for finding and retrieving other similar candidates likelyto perform well. The individual distinctive diphones can also be storedas desired for later use in a set 396.

In some embodiments it may be desirable to construct a statisticallanguage model of machine TTS system behavior to help uniquely identifyparticular machine entities, and to better characterize overall expectedmachine articulations. This machine SLM 380 in turn can be used by themachine articulation comparator 231 (discussed above) to furtheruniquely identify a machine imposter rendition of a challenge sentence.To further train and refine the SLM 380 the system could solicitrepeated samples of the same words and phrases to better characterize asignature of a particular unique machine. Moreover to further train theSLM to behave like a human ear, the set of sentences which are uniformlyrejected at a high rate by humans can be used as a seed set fortraining. This will have the effect of biasing the SLM 380 to hearcontent that is preferred by the human ear for discriminating against amachine imposter.

Through sufficient training samples the system should be able toidentify, with controllable confidence levels, an appropriate set ofsentences that are likely to weed out a machine imposter. Moreovercandidate sentences which are confusing or take too long for a human canbe eliminated as well. Again it is preferable that the challengesentences include primarily samples that are rapidly processed andarticulated as measured against a human reference set. The respectivetimes required by human and machines can also be measured and compiledto determine minimum, maximum, average, mean and threshold times. Forexample, it may be desirable to select challenge sentences in which thetime difference between human and machine articulations is greatest.

Embodiments of the invention may be used with machines, devices andsystems which use a speaker verifier option in accordance with the mediaresource control protocol (MRCPv2). This protocol allows for speakerverification using a voice authentication methodology that can be usedto identify the speaker in order to grant the user access to sensitiveinformation and transactions. It will be apparent from those skilled inthe art that this same protocol could be adapted (including with a newdedicated command) to initiate requests for verification of an entity asa human or an authorized machine agent.

As the proliferation of speech recognition based applications continues,the present invention can be used to detect what might be referred to as“cluck” fraud in the form of improper selections of ads, access toaccounts, etc. This would be a superior form of fraud detection sincethere is no practical mechanism for detecting if a keyboard/mouseselection of an ad is fraudulent.

Finally, it will be apparent to those skilled in the art that themethods of the present invention, including those illustrated in FIGS.1, 2, 3 and 4 can be implemented using any one of many known programminglanguages suitable for creating applications that can run on clientsystems, and large scale computing systems, including servers connectedto a network (such as the Internet). The details of the specificimplementation of the present invention will vary depending on theprogramming language(s) used to embody the above principles, and are notmaterial to an understanding of the present invention.

The above descriptions are intended as merely illustrative embodimentsof the proposed inventions. It is understood that the protectionafforded the present invention also comprehends and extends toembodiments different from those above, but which fall within the scopeof the present claims.

What is claimed is: 1.-19. (canceled)
 20. A method embodied in a computer readable medium of selecting challenge data to be used for accessing data and/or resources of a computing system comprising: (a) providing data identifying a first set of diphones to be assessed by a computing system, wherein each of said first set of diphones represents a sound associated with an articulation of a pair of phonemes in a natural language; (b) generating an a plurality of articulation scores using the computing system based on measuring acoustical characteristics of a machine text to speech (TTS) system articulation of each of said first set of diphones; and (c) selecting challenge text including words and phrases from the natural language using the computing system based on said plurality of articulation scores; wherein said challenge text is useable by an utterance-based challenge system for discriminating between humans and machines.
 21. The method of claim 20 further including a step: processing input speech by an entity using said challenge item database to distinguish between a human and a machine synthesized voice.
 22. A method embodied in a computer readable medium of selecting challenge data to be used for accessing data and/or resources of a computing system comprising: a) selecting a candidate challenge item which includes text words and/or visual images; b) measuring first acoustical characteristics of a computer synthesized utterance when articulating challenge content associated with said candidate challenge item; c) measuring second acoustical characteristics of a human utterance when articulating said challenge content; d) generating a challenge item score based on measuring a difference in said first and second acoustical characteristics; and e) designating said candidate challenge item as a reference challenge item when said challenge item score exceeds a target threshold.
 23. The method of claim 22 further including a step: processing input speech by an entity using said reference challenge item to distinguish between a human and a machine synthesized voice.
 24. The method of claim 22, wherein said challenge item score is also based on one or more topics associated with said text words and/or visual images and which are identified and measured in said computer synthesized utterance and human utterance respectively.
 25. The method of claim 22, wherein said challenge item score is also based on prosodic elements associated with said text words and/or visual images and which are identified and measured in said computer synthesized utterance and human utterance respectively.
 26. The method of claim 22, wherein said challenge item score is also based on a collaborative filtering score generated by measuring responses to a sequence of two or more of said candidate challenge items identified in said in said computer synthesized utterances and human utterances respectively.
 27. The method of claim 26 wherein said collaborative filtering score is derived by identifying at least a first reference correlation between at least a first reference text descriptor for a first challenge item and a second reference text descriptor for a second challenge item, including a probability that a human reviewer identifying said first reference text descriptor when presented with said first challenge item also provides said second reference text descriptor when presented with said second challenge item, or vice-versa.
 28. The method of claim 26 wherein said collaborative filtering score is derived by identifying at least a first reference correlation between a first challenge item presented in incomplete form, and a predicted response for completing said challenge item.
 29. A method embodied in a computer readable medium of selecting challenge data to be used for accessing data and/or resources of a computing system comprising: a) defining a plurality of demographic groups, said demographic groups being based on one or more of age, sex, or domicile; b) providing a plurality of CAPTCHA (Completely Automatic Public Turing Test To Tell Humans And Computers Apart) challenge items consisting of a combination of images and solicited utterances with the computing system; c) for each of said challenge items, using the computing system to compare a first reference acoustic response of a machine entity and a second reference acoustic response provided by a representative of said demographic group; and d) for each demographic group, selecting an optimal set of CAPTCHA challenge items determined by the computing system to yield the greatest acoustic response difference over said machine entity.
 30. The method of claim 29 further including a step: identifying a demographic group for an entity; and processing input speech by said entity using at least one of said optimal set of CAPTCHA challenge items for said demographic group to determine if it is a human or a machine synthesized voice.
 31. (canceled)
 32. A system for identifying challenge data to be used for accessing data and/or resources of a computing system comprising: one or more software routines embodied in a computer readable medium adapted to cause the computing system to: (a) provide data identifying a first set of diphones to be assessed by a computing system, wherein each of said first set of diphones represents a sound associated with an articulation of a pair of phonemes in a natural language; (b) generate a plurality of articulation scores based on measuring acoustical characteristics of a machine text to speech (TTS) system articulation of each of said first set of diphones; and (c) select challenge text including words and phrases from the natural language using the computing system based on said plurality of articulation scores; wherein said challenge text is useable by an utterance-based challenge system for discriminating between humans and machines.
 33. A system for identifying challenge data to be used for accessing data and/or resources of a computing system comprising: one or more software routines embodied in a computer readable medium adapted to cause the computing system to: a) select a candidate challenge item which includes text words and/or visual images; b) measure first acoustical characteristics of a computer synthesized utterance when articulating challenge content associated with said candidate challenge item; c) measure second acoustical characteristics of a human utterance when articulating said challenge content; d) generate a challenge item score based on measuring a difference in said first and second acoustical characteristics; and e) designate said candidate challenge item as a reference challenge item when said challenge item score exceeds a target threshold.
 34. A system for identifying challenge data to be used for accessing data and/or resources of a computing system comprising: one or more software routines embodied in a computer readable medium adapted to cause the computing system to: a) define a plurality of demographic groups, said demographic groups being based on one or more of age, sex, or domicile; b) provide a plurality of CAPTCHA (Completely Automatic Public Turing Test To Tell Humans And Computers Apart) challenge items consisting of a combination of images and solicited utterances; c) for each of said challenge items, compare a first reference response of a machine entity and a second reference response provided by a representative of said demographic group; and d) for each demographic group, select an optimal set of CAPTCHA challenge items determined by the computing system to yield the greatest response difference over said machine entity.
 35. The method of claim 20 wherein said acoustical characteristics include measurements of transition differences between phonemes.
 36. The method of claim 20 wherein said challenge text includes sentences extracted from a corpus of human-authored documents.
 37. The method of claim 20 wherein said challenge text includes sentences automatically generated from said natural language by the computing system based on said diphone articulation scores.
 38. The method of claim 20 wherein said word and phrases are given articulation scores. 